How many types of data annotation?

Data annotation is a broad practice but every type of data has a labeling process associated with it. Here are some of the most common types:

Semantic annotation: Semantic annotation is a process where concepts like people, places or company names are labeled within a text to help machine learning models categorize new concepts in future texts. This is a key part of AI training to improve chatbots and search relevance.
Image annotation: This type of annotation ensures that machines recognize an annotated area as a distinct object and often involves bounding boxes (imaginary boxes drawn on an image) and semantic segmentation (the assignment of meaning to every pixel). These labeled datasets can be used to guide autonomous vehicles or as part of facial recognition software.
Video annotation: Similar to image annotation, video annotation uses techniques like bounding boxes but on a frame-by-frame bases, or via a video annotation tool, to acknowledge movement. Data uncovered through video annotation is key for computer vision models that conduct localization and object tracking.
Text categorization: Text categorization is the process of assigning categories to sentences or paragraphs by topic, within a given document.
Entity annotation: The process of helping a machine to understand unstructured sentences. There are a wide variety of techniques that can be utilized to establish a greater understanding such as Named Entity Recognition (NER), where words within a body of text are annotated with predetermined categories (e.g., person, place or thing). Another example is entity linking, where parts of a text (e.g., a company and the place where it’s headquartered) are tagged as related.
Intent extraction: Intent extraction is the process of labeling phrases or sentences with intent in order to build a library of ways people use certain verbiage. For example, “How do I make a reservation?” and “Can I confirm my reservation,” both contain the same keyword, but have different intent. It’s another key tool for teaching chatbot algorithms to make decisions about customer requests.
Phrase chunking: phrase chunking involves tagging parts of speech with its grammatical definition (e.g., noun or verb).

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Our data annotation experience spans over 5 years, providing our expertise in training data for countless projects on a global scale. By combining our human-assisted approach with machine-learning assistance, we give you the high-quality training data you need.

Our text annotation, image annotation, audio annotation, and video annotation will give you the confidence to deploy your AI and ML models at scale. Whatever your data annotation needs may be, our platform and managed service team are standing by to assist you in both deploying and maintaining your AI and ML projects.

Interested in learning more about our data annotation services? Contact us today and one of our highly trained team members will reach out to you as soon as possible.